Exploring librarians' practices when teaching advanced searching for knowledge synthesis: results from an online survey.

Objective: There is little research available regarding the instructional practices of librarians who support students completing knowledge synthesis projects. This study addresses this research gap by identifying the topics taught, approaches, and resources that academic health sciences librarians employ when teaching students how to conduct comprehensive searches for knowledge synthesis projects in group settings. Methods: This study applies an exploratory-descriptive design using online survey data collection. The final survey instrument included 31 open, closed, and frequency-style questions. Results: The survey received responses from 114 participants, 74 of whom met the target population. Some key results include shared motivations to teach in groups, including student learning and curriculum requirements, as well as popular types of instruction such as single session seminars, and teaching techniques, such as lectures and live demos. Conclusion: This research demonstrates the scope and coverage of librarian-led training in the knowledge synthesis research landscape. Although searching related topics such as Boolean logic were the most frequent, librarians report teaching throughout the review process like methods and reporting. Live demos and lectures were the most reported approaches to teaching, whereas gamification or student-driven learning were used rarely. Our results suggest that librarian's application of formal pedagogical approaches while teaching knowledge synthesis may be under-utilized, as most respondents did not report using any formal instructional framework.

Digital health literacy and its determinants among community dwelling elderly people in Taiwan.

Background: Digital Health Literacy (DHL) is crucial in navigating digital health environments, yet few studies focus on older adults. Objective: Explore the associations of digital health information and resource utilization, IT-related social support, and barriers/enhancers to digital health service usage with DHL among older adults. Methods: A cross-sectional study was conducted from January 2022 to April 2023, involving 417 individuals over the age of 60 who were interviewed using an instrument for collecting data on DHL, social support, barriers/enhancers influencing use of digital health resources and personal/demographic data. Multi-regression models were used to examine the associations. Results: Higher DHL scores were associated with daily use of digital interaction with healthcare (B = 0.28; 95% confidence interval [CI] = 0.07, 0.49; p = .01), daily use of other digital health resources (B = 0.22; 95% CI = 0.05, 0.40; p = .01), ease in finding assistance for online navigation (B = 0.27; 95% CI = 0.08, 0.45; p = .01), self-perceived digital proficiency ("usually very good at surfing the internet," B = 0.35; 95% CI = 0.18, 0.52; p = .01), assistance from relatives/others in internet browsing (B = 0.20; 95% CI = 0.02, 0.37; p = .02), and having access to a computer, tablet, or smartphone (B = 0.29; 95% CI = 0.11, 0.47, p = .01). Conversely, barriers like "no access to a computer, tablet, or phone at all times" (B = -0.19; 95% CI = -0.34, -0.04; p = .01), "difficulty understanding online content" (B = -0.22; 95% CI = -0.36, -0.07; p = .01), and "believing to be too old for online services" (B = -0.18; 95% CI = -0.32, -0.03; p = .02) were associated with lower DHL scores. Conclusions: Engagement with digital health platforms, including making online appointments and accessing personal health records, is associated with higher DHL levels. Support from relatives or others, a modifiable attribute, is also associated with elevated DHL among older adults.

Structured Stochastic Curve Fitting without Gradient Calculation.

Optimization of parameters and hyperparameters is a general process for any data analysis. Because not all models are mathematically well-behaved, stochastic optimization can be useful in many analyses by randomly choosing parameters in each optimization iteration. Many such algorithms have been reported and applied in chemistry data analysis, but the one reported here is interesting to check out, where a naïve algorithm searches each parameter sequentially and randomly in its bounds. Then it picks the best for the next iteration. Thus, one can ignore irrational solution of the model itself or its gradient in parameter space.

Utilizing Game-Based Learning in Literature Searching Instruction for Pharmacy Students.

OBJECTIVE: To determine whether gamifying librarian-led literature searching instruction improved student performance on an authentic literature searching assessment. Secondary objectives included determination of effect on email requests for assistance and student confidence in literature searching abilities. METHODS: Literature searching in PubMed is taught by a librarian to first-year pharmacy students in a drug information course over a 2-week period. The librarian chose to implement 2 game-based learning activities in the live lecture sessions: a crossword puzzle and an escape room. To increase engagement, students were encouraged to work collaboratively as a team during class. To evaluate the impact of incorporating gamification into literature searching instruction, the authors evaluated student grades on a literature searching assignment, reviewed the number of emails received asking for assistance, and evaluated student confidence in literature searching. RESULTS: Students scored higher on their literature searching assignment after the implementation of game-based instruction. The average grade on this assignment in 2022 was 90.1% compared with 2021 when the average was 79.9%. The average of 90.6% in 2023 also showed statistically significant improvement compared with 2021. Email requests decreased and student confidence increased when comparing 2021 outcomes with those in 2022 and 2023. CONCLUSION: Overall, the gamification of literature searching instruction in this course appears to have increased student assignment scores and was well-received by the students.

Nursing Staff's Perspectives of Care Robots for Assisted Living Facilities: Systematic Literature Review.

BACKGROUND: Care robots have been proposed in response to nursing shortages in assisted living facilities (ALFs) and the growing population of older adults. While the use of care robots may improve the general health and well-being of older adults, their introduction changes the work of nursing staff fundamentally, and it has implications for the entire health care system. In developing such technology, it is important to include end users, but so far, the nursing staff's perspectives have largely been ignored. OBJECTIVE: This study aims to examine the literature on nursing staff's attitudes, needs, and preferences related to the use of care robots in ALFs, in order to discover gaps in the literature and guide future research. METHODS: This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 protocol. On May 12, 2023, we searched PubMed, CINAHL Plus with Full Text, PsycINFO, the IEEE Xplore Digital Library, and the ACM Digital Library using predetermined search terms. Included publications, written in English, focused on the predevelopment phase, in which information was gathered on nursing staff's attitudes, needs, and preferences regarding care robots for ALFs. Publications were excluded if they did not provide peer-reviewed empirical data. The studies' findings were summarized, coded, and analyzed into major themes using thematic analysis and narrative synthesis. Their quality was assessed using McGill University's Mixed Methods Appraisal Tool and the Joanna Briggs Institute's critical appraisal tools. RESULTS: The final sample included 15 studies. Most of the studies (n=11, 73%) were rated as good quality; however, there was a general lack of reporting on important methodological decisions and sample characteristics. Nursing staff desired care robots that could assist with physically demanding tasks and reduce their workload but had mixed feelings on whether robots could or should assist with social tasks. In addition, nursing staff are concerned about the ethics of care robots, as well as about their safety, accessibility, and operability. The nursing staff's culture, qualification, and role in the facility may influence their perspectives of care robots. The studies lacked theory-driven designs and large sample sizes. Eight (53%) studies mentioned using a participatory design approach, but a lack of established criteria for what constitutes participatory design leads to varying degrees of methodological quality. CONCLUSIONS: There was consensus among nursing staff that care robots should serve as nursing assistants to reduce workload. Whether robots could or should assist with social tasks remains a question. Further research is needed to mitigate nursing staff's concerns and understand the socioecological factors that influence their perspectives of care robots and their adoption in ALFs. In addition, theory-driven and large sample size study designs are necessary, as well as work to develop clear criteria for related participatory design research.

Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review.

BACKGROUND: Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice. OBJECTIVE: This scoping review aimed to explore methods for identifying patients with postoperative infections using electronic health record (EHR) data to go beyond the reference standard of manual chart review. METHODS: We performed a systematic search strategy across PubMed, Embase, Web of Science (Core Collection), the Cochrane Library, and Emcare (Ovid), targeting studies addressing the prediction and fully automated surveillance (ie, without manual check) of diverse bacterial infections in the postoperative setting. For prediction modeling studies, we assessed the labeling methods used, categorizing them as either manual or automated. We evaluated the different types of EHR data needed for the surveillance and labeling of postoperative infections, as well as the performance of fully automated surveillance systems compared with manual chart review. RESULTS: We identified 75 different methods and definitions used to identify patients with postoperative infections in studies published between 2003 and 2023. Manual labeling was the predominant method in prediction modeling research, 65% (49/75) of the identified methods use structured data, and 45% (34/75) use free text and clinical notes as one of their data sources. Fully automated surveillance systems should be used with caution because the reported positive predictive values are between 0.31 and 0.76. CONCLUSIONS: There is currently no evidence to support fully automated labeling and identification of patients with infections based solely on structured EHR data. Future research should focus on defining uniform definitions, as well as prioritizing the development of more scalable, automated methods for infection detection using structured EHR data.

Unraveling the Hemolytic Toxicity Tapestry of Peptides using Chemical Space Complex Networks.

Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.

Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking Applications.

Template matching is a common approach in bin-picking tasks. However, it often struggles in complex environments, such as those with different object poses, various background appearances, and varying lighting conditions, due to the limited feature representation of a single template. Additionally, during the bin-picking process, the template needs to be frequently updated to maintain detection performance, and finding an adaptive template from a vast dataset poses another challenge. To address these challenges, we propose a novel template searching method in a latent space trained by a Variational Auto-Encoder (VAE), which generates an adaptive template dynamically based on the current environment. The proposed method was evaluated experimentally under various conditions, and in all scenarios, it successfully completed the tasks, demonstrating its effectiveness and robustness for bin-picking applications. Furthermore, we integrated our proposed method with YOLO, and the experimental results indicate that our method effectively improves YOLO's detection performance.

User Requirements for an Electronic Patient Recruitment System: Semistructured Interview Analysis After First Implementation in 3 German University Hospitals.

BACKGROUND: Clinical trials are essential for medical research and medical progress. Nevertheless, trials often fail to reach their recruitment goals. Patient recruitment systems aim to support clinical trials by providing an automated search for eligible patients in the databases of health care institutions like university hospitals. To integrate patient recruitment systems into existing workflows, previous works have assessed user requirements for these tools. In this study, we tested patient recruitment systems KAS+ and recruIT as part of the MIRACUM (Medical Informatics in Research and Care in University Medicine) project. OBJECTIVE: Our goal was to investigate whether and to what extent the 2 different evaluated tools can meet the requirements resulting from the first requirements analysis, which was performed in 2018-2019. A user survey was conducted to determine whether the tools are usable in practice and helpful for the trial staff. Furthermore, we investigated whether the test phase revealed further requirements for recruitment tools that were not considered in the first place. METHODS: We performed semistructured interviews with 10 participants in 3 German university hospitals who used the patient recruitment tools KAS+ or recruIT for at least 1 month with currently recruiting trials. Thereafter, the interviews were transcribed and analyzed by Meyring method. The identified statements of the interviewees were categorized into 5 groups of requirements and sorted by their frequency. RESULTS: The evaluated recruIT and KAS+ tools fulfilled 7 and 11 requirements of the 12 previously identified requirements, respectively. The interviewed participants mentioned the need for different notification schedules, integration into their workflow, different patient characteristics, and pseudonymized screening lists. This resulted in a list of new requirements for the implementation or enhancement of patient recruitment systems. CONCLUSIONS: Trial staff report a huge need of support for the identification of eligible trial participants. Moreover, the workflows in patient recruitment differ across trials. For better suitability of the recruitment systems in the workflow of different kinds of trials, we recommend the implementation of an adjustable notification schedule for screening lists, a detailed workflow analysis, broad patient filtering options, and the display of all information needed to identify the persons on the list. Despite criticisms, all participants confirmed to use the patient recruitment systems again.

Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No).

Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active-while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules.

The Tromsø Study: Lessons Learnt About Searching for Information About Health and Illness.

INTRODUCTION: Norway has a high use of e-health. METHODS: This paper summarizes and discusses the published data from the Tromsø 7 Study, conducted between 2015 and 2016, focusing on e-health utilization in the Norwegian population aged 40 and above. RESULTS: More than half of the participants reported using the Internet for health purposes. The main channels for obtaining information were search engines, apps, social media platforms, and online videos. The respondents frequently acted upon the information obtained online, and online health information influenced decisions regarding healthcare utilization and treatment management. Most respondents indicated a positive reaction to the information found online. CONCLUSIONS: The Tromsø 7 Study highlights the widespread utilization of e-health in Norway. The study also emphasizes the significant impact of e-health on individuals' decision-making processes related to their health. The findings suggest that the use of e-health overall does not replace the use of traditional health services, but rather functions as a supplement. Most respondents report positive reactions to online health information, highlighting the importance and relevance of e-health in modern healthcare practices.

Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review.

BACKGROUND: Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care. OBJECTIVE: This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings. METHODS: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O'Malley's methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria. RESULTS: In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth. CONCLUSIONS: All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested.

Automation of duplicate record detection for systematic reviews: Deduplicator.

BACKGROUND: To describe the algorithm and investigate the efficacy of a novel systematic review automation tool "the Deduplicator" to remove duplicate records from a multi-database systematic review search. METHODS: We constructed and tested the efficacy of the Deduplicator tool by using 10 previous Cochrane systematic review search results to compare the Deduplicator's 'balanced' algorithm to a semi-manual EndNote method. Two researchers each performed deduplication on the 10 libraries of search results. For five of those libraries, one researcher used the Deduplicator, while the other performed semi-manual deduplication with EndNote. They then switched methods for the remaining five libraries. In addition to this analysis, comparison between the three different Deduplicator algorithms ('balanced', 'focused' and 'relaxed') was performed on two datasets of previously deduplicated search results. RESULTS: Before deduplication, the mean library size for the 10 systematic reviews was 1962 records. When using the Deduplicator, the mean time to deduplicate was 5 min per 1000 records compared to 15 min with EndNote. The mean error rate with Deduplicator was 1.8 errors per 1000 records in comparison to 3.1 with EndNote. Evaluation of the different Deduplicator algorithms found that the 'balanced' algorithm had the highest mean F1 score of 0.9647. The 'focused' algorithm had the highest mean accuracy of 0.9798 and the highest recall of 0.9757. The 'relaxed' algorithm had the highest mean precision of 0.9896. CONCLUSIONS: This demonstrates that using the Deduplicator for duplicate record detection reduces the time taken to deduplicate, while maintaining or improving accuracy compared to using a semi-manual EndNote method. However, further research should be performed comparing more deduplication methods to establish relative performance of the Deduplicator against other deduplication methods.

Scoping review search practices in the social sciences: A scoping review.

A thorough literature search is a key feature of scoping reviews. We investigated the search practices used by social science researchers as reported in their scoping reviews. We collected scoping reviews published between 2015 and 2021 from Social Science Citation Index. In the 2484 included studies, we observed a 58% average annual increase in published reviews, primarily from clinical and applied social science disciplines. Bibliographic databases comprised most of the information sources in the primary search strategy (n = 9565, 75%), although reporting practices varied. Most scoping reviews (n = 1805, 73%) included at least one supplementary search strategy. A minority of studies (n = 713, 29%) acknowledged an LIS professional and few listed one as a co-author (n = 194, 8%). We conclude that to improve reporting and strengthen the impact of the scoping review method in the social sciences, researchers should consider (1) adhering to PRISMA-S reporting guidelines, (2) employing more supplementary search strategies, and (3) collaborating with LIS professionals.

Searchsmart.org: Guiding researchers to the best databases and search systems for systematic reviews and beyond.

When searching for scholarly documents, researchers often stick with the same familiar handful of databases. Yet, just beyond these limited horizons lie dozens of alternatives with which they could search more effectively, whether for quick lookups or thorough searches in systematic reviews or meta-analyses. Searchsmart.org is a free website that guides researchers to particularly suitable search options for their particular disciplines, offering a wide array of resources, including search engines, aggregators, journal platforms, repositories, clinical trials databases, bibliographic databases, and digital libraries. Search Smart currently evaluates the coverage and functionality of more than a hundred leading scholarly databases, including most major multidisciplinary databases and many that are discipline-specific. Search Smart's primary use cases involve database-selection decisions as part of systematic reviews, meta-analyses, or bibliometric analyses. Researchers can use up to 583 criteria to filter and sort recommendations of databases and the interfaces through which they can be accessed for user-friendliness, search rigor, or relevance. With specific pre-defined filter settings, researchers can quickly identify particularly suitable databases for Boolean keyword searching and forward or backward citation searching. Overall, Search Smart's recommendations help researchers to discover knowledge more effectively and efficiently by selecting the more suitable databases for their tasks.

Guidance on how to efficiently find, choose, and use available systematic reviews was developed.

OBJECTIVES: The aim of this paper is to provide clinicians and authors of clinical guidelines or patient information with practical guidance on searching and choosing systematic reviews(s) (SR[s]) and, where adequate, on making use of SR(s). STUDY DESIGN AND SETTING: At the German conference of the Evidence-Based Medicine Network (EbM Network) a workshop on the topic was held to identify the most important areas where guidance for practice appears necessary. After the workshop, we established working groups. These included SR users with different backgrounds (eg, information specialists, epidemiologists) and working areas. Each working group developed and consented a draft guidance based on their expert knowledge and experiences. The results were presented to the entire group and finalized in an iterative process. RESULTS: We developed a practical guidance that answers questions that usually arise when choosing and using SR(s). (1) How to efficiently find high-quality SRs? (2) How to choose the most appropriate SR? (3) What to do if no SR of sufficient quality could be identified? In addition, we developed an algorithm that links these steps and accounts for their interaction. The resulting guidance is primarily directed at clinicians and developers of clinical practice guidelines or patient information resources. CONCLUSION: We suggest practical guidance for making the best use of SRs when answering a specific research question. The guidance may contribute to the efficient use of existing SRs. Potential benefits when using existing SRs should be always weighted against potential limitations.

Overwintering West Nile virus in active Culex pipiens mosquito populations in Greece.

The flavivirus West Nile Virus (WNV), which is transmitted by mosquitoes, poses a significant threat to both humans and animals, and its outbreaks often challenge public health in Europe and other continents. In recent years, there is an increasing trend of WNV incidence rates across several European countries. However, whether there is a year-round circulation or seasonal introduction has yet to be elucidated. Real-time polymerase chain reaction (PCR) identified WNV-positive Culex pipiens mosquitos in 6 out of 146 pools examined in winter 2022 that correspond to three out of the 24 study areas, located in two coastal regions units in Attica, Greece. Spatial dispersion of the six positive pools in the same region suggests a clustered circulation of WNV during the winter of 2022. This is the first study that documents the identification of WNV in Cx. pipiens populations, captured in adult traps during winter period. Our findings underscore the need to extend entomological surveillance programs to include the winter period, specifically in temperate climates and historically affected areas by WNV.

Effectiveness of eHealth Interventions in Improving Medication Adherence Among Patients With Cardiovascular Disease: Systematic Review and Meta-Analysis.

BACKGROUND: Nonadherence to medication among patients with cardiovascular diseases undermines the desired therapeutic outcomes. eHealth interventions emerge as promising strategies to effectively tackle this issue. OBJECTIVE: The aim of this study was to conduct a network meta-analysis (NMA) to compare and rank the efficacy of various eHealth interventions in improving medication adherence among patients with cardiovascular diseases (CVDs). METHODS: A systematic search strategy was conducted in PubMed, Embase, Web of Science, Cochrane, China National Knowledge Infrastructure Library (CNKI), China Science and Technology Journal Database (Weipu), and WanFang databases to search for randomized controlled trials (RCTs) published from their inception on January 15, 2024. We carried out a frequentist NMA to compare the efficacy of various eHealth interventions. The quality of the literature was assessed using the risk of bias tool from the Cochrane Handbook (version 2.0), and extracted data were analyzed using Stata16.0 (StataCorp LLC) and RevMan5.4 software (Cochrane Collaboration). The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. RESULTS: A total of 21 RCTs involving 3904 patients were enrolled. The NMA revealed that combined interventions (standardized mean difference [SMD] 0.89, 95% CI 0.22-1.57), telephone support (SMD 0.68, 95% CI 0.02-1.33), telemonitoring interventions (SMD 0.70, 95% CI 0.02-1.39), and mobile phone app interventions (SMD 0.65, 95% CI 0.01-1.30) were statistically superior to usual care. However, SMS compared to usual care showed no statistical difference. Notably, the combined intervention, with a surface under the cumulative ranking curve of 79.3%, appeared to be the most effective option for patients with CVDs. Regarding systolic blood pressure and diastolic blood pressure outcomes, the combined intervention also had the highest probability of being the best intervention. CONCLUSIONS: The research indicates that the combined intervention (SMS text messaging and telephone support) has the greatest likelihood of being the most effective eHealth intervention to improve medication adherence in patients with CVDs, followed by telemonitoring, telephone support, and app interventions. The results of these network meta-analyses can provide crucial evidence-based support for health care providers to enhance patients' medication adherence. Given the differences in the design and implementation of eHealth interventions, further large-scale, well-designed multicenter trials are needed. TRIAL REGISTRATION: INPLASY 2023120063; https://inplasy.com/inplasy-2023-12-0063/.

Investigating the Use of Serious Games for Cancer Control Among Children and Adolescents: Scoping Review.

BACKGROUND: Effective health care services that meet the diverse needs of children and adolescents with cancer are required to alleviate their physical, psychological, and social challenges and improve their quality of life. Previous studies showed that serious games help promote people's health. However, the potential for serious games to be used for successful cancer control for children and adolescents has received less attention. OBJECTIVE: This scoping review aimed to map the use of serious games in cancer prevention and cancer care for children and adolescents, and provide future directions for serious games' development and implementation within the context of cancer control for children and adolescents. METHODS: This study followed a combination of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and the JBI (Joanna Briggs Institute) framework for the conduct of scoping reviews. PubMed, CINAHL Plus Full Text, Scopus, Web of Science Core Collection, and American Psychological Association (APA) PsycINFO databases were used for the search. RESULTS: From the initial 2750 search results, 63 papers were included in the review, with 28 quantitative, 14 qualitative, and 21 mixed method studies. Most of the studies were cancer care serious game papers (55/63, 87%) and a small number of studies were cancer prevention serious game papers (8/63, 13%). The majority of the included studies were published between 2019 and 2023 (cancer prevention: 5/8, 63%; cancer care: 35/55, 64%). The majority of the studies were conducted in Europe (cancer prevention: 3/8, 38%; cancer care: 24/55, 44%) and North America (cancer prevention: 4/8, 50%; cancer care: 17/55, 31%). Adolescents were the most represented age group in the studies' participants (cancer prevention: 8/8, 100%; cancer care: 46/55, 84%). All (8/8, 100%) cancer prevention serious game papers included healthy people as participants, and 45 out of 55 (82%) cancer care serious game papers included patients with cancer. The majority of cancer prevention serious game papers addressed game preference as a target outcome (4/8, 50%). The majority of cancer care serious game papers addressed symptom management as a target outcome (28/55, 51%). Of the cancer care studies examining serious games for symptom management, the majority of the studies were conducted to treat psychological (13/55, 24%) and physical symptoms (10/55, 18%). CONCLUSIONS: This review shows both the growth of interest in the use of serious games for cancer control among children and adolescents and the potential for bias in the relevant literature. The diverse characteristics of the included papers suggest that serious games can be used in various ways for cancer control among children and adolescents while highlighting the need to develop and implement serious games in underrepresented areas.

Searches as data: archiving and sharing search strategies using an institutional data repository.

Background: By defining search strategies and related database exports as code/scripts and data, librarians and information professionals can expand the mandate of research data management (RDM) infrastructure to include this work. This new initiative aimed to create a space in McGill University's institutional data repository for our librarians to deposit and share their search strategies for knowledge syntheses (KS). Case Presentation: The authors, a health sciences librarian and an RDM specialist, created a repository collection of librarian-authored knowledge synthesis (KS) searches in McGill University's Borealis Dataverse collection. We developed and hosted a half-day "Dataverse-a-thon" where we worked with a team of health sciences librarians to develop a standardized KS data management plan (DMP), search reporting documentation, Dataverse software training, and howto guidance for the repository. Conclusion: In addition to better documentation and tracking of KS searches at our institution, the KS Dataverse collection enables sharing of searches among colleagues with discoverable metadata fields for searching within deposited searches. While the initial creation of the DMP and documentation took about six hours, the subsequent deposit of search strategies into the institutional data repository requires minimal effort (e.g., 5-10 minutes on average per deposit). The Dataverse collection also empowers librarians to retain intellectual ownership over search strategies as valuable stand-alone research outputs and raise the visibility of their labor. Overall, institutional data repositories provide specific benefits in facilitating compliance both with PRISMA-S guidance and with RDM best practices.